PR000005 Questions & Answers & PR000005 Study Guide & PR000005 Exam Preparation

Informatica PR000005 : Data Quality 9.x Developer Specialist

Exam Code: PR000005

Exam Name: Data Quality 9.x Developer Specialist

Updated: Aug 23, 2026

Q & A: 70 Questions and Answers

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About Informatica PR000005 Exam Preparation

Read the Informatica Data Quality 9.x Developer (PR000005) Certification Exam formate below

  • Passing score: 70%
  • Language: English
  • Format: Multiple choices, multiple answers
  • Number of Questions: 70
  • Length of Examination: 90 minutes

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Informatica provides the market-leading data integration platform. There are 500,000 combinations of applications and platforms tested. The data integration platform works with the widest possible range of disparate standards, systems, and applications. This impartial and universal vision makes Informatica unique on the current market as a leader in the data integration platform. So, Informatica becomes the ideal strategic platform for companies wishing to solve data integration problems of any size.

To Pass Data Quality 9.x Developer Specialist Certification, a candidate must pass a written, supervised in-person or webcam exam. The specialized test confirms that the person understands the product and can apply the skills and abilities necessary to contribute to a project as a member of the complete team.

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Read the Informatica Data Quality 9.x Developer (PR000005) Certification Exam Requirements below

There is no perquisites for Informatica Data Quality 9.x Developer (PR000005) exam.

Reference: https://www.informatica.com/services-and-training/certification/data-quality-certification.html#fbid=M68tJ9Yt3El

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Informatica PR000005 Exam Syllabus Topics:

SectionObjectives
Topic 1: Data Cleansing and Validation- Improve data quality
  • 1. Cleanse and enrich data
    • 2. Create validation rules
      • 3. Handle exceptions
        Topic 2: Developer Tool and Mappings- Build Data Quality solutions
        • 1. Configure transformations
          • 2. Create mappings and mapplets
            • 3. Deploy and execute applications
              Topic 3: Matching and Consolidation- Implement matching strategies
              • 1. Use probabilistic and deterministic matching
                • 2. Configure match rules
                  • 3. Group and consolidate duplicate records
                    Topic 4: Data Profiling- Analyze data quality
                    • 1. Profile column data
                      • 2. Identify data anomalies and patterns
                        • 3. Profile primary and foreign keys
                          Topic 5: Data Quality Applications- Implement enterprise data quality processes
                          • 1. Data quality monitoring and reporting
                            • 2. Web service deployment
                              • 3. Data Quality for Excel integration
                                Topic 6: Data Standardization- Standardize and parse data
                                • 1. Apply reference tables and dictionaries
                                  • 2. Use Standardizer transformation
                                    • 3. Use Token Parser and Pattern Parser

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